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Bayesian smoothing under structural measurement error model with multiple covariates
Journal of the Korean Data & Information Science Society 2017;28:709-20
Published online May 31, 2017
© 2017 Korean Data & Information Science Society.

Jinseub Hwang1 · Dal Ho Kim2

1Department of Computer Science and Statistics, Daegu University
2Department of Statistics, Kyungpook National University
Correspondence to: Dal Ho Kim
Professor, Department of Statistics, Kyungpook National University, Daegu 41566, Korea. E-mail: dalkim@knu.ac.kr
Received April 17, 2017; Revised May 23, 2017; Accepted May 23, 2017.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
In healthcare and medical research, many important variables have a measurement error such as body mass index and laboratory data. It is also not easy to collect samples of large size because of high cost and long time required to collect the target patient satisfied with inclusion and exclusion criteria. Beside, the demand for solving a complex scientific problem has highly increased so that a semiparametric regression approach could be of substantial value solving this problem. To address the issues of measurement error, small domain and a scientific complexity, we conduct a multivariable Bayesian smoothing under structural measurement error covariate in this article. Specifically we enhance our previous model by incorporating other useful auxiliary covariates free of measurement error. For the regression spline, we use a radial basis functions with fixed knots for the measurement error covariate. We organize a fully Bayesian approach to fit the model and estimate parameters using Markov chain Monte Carlo. Simulation results represent that the method performs well. We illustrate the results using a national survey data for application.
Keywords : Bayes, multivariable, radial basis functions, small area, structural measurement error.